Life in Pharma: Tables, Tables Everywhere

junco as case study

Challenges in Pharmaceutical Programming

The pharmaceutical industry faces unique challenges in clinical and statistical programming:

• Implementing patterns used across many table shells (developed by dedicated teams)
• Company-specific statistical methods that need to be standardized
• Complex table structures that must be consistent across studies
• Need for a core framework that ensures all company shells can be created consistently

Shells Are Table Specific, Table Creation Is Not

The Junco Package Approach

Our solution was to develop our own business logic framework for J&J table creation

Getting to Production

The Junco package provides key features needed for production-ready clinical tables:

• True-type font support: Word wrapping, pagination, and RTF export
• Higher order column counts: Utilities for spanning column headers
• Guaranteed pathability in row space
• Nearest-value (SAS-like) rounding support: Maintaining consistency with existing processes
• Statistical calculations: In accord with business logic
• Robust approach for risk diff columns

Creating the package

From Scripts to Standardized Tools

Code Design and API for Users

Once we had the core functionality working, we needed to focus on:

• User-friendly API: Creating intuitive functions that match users’ mental models
• Consistent interfaces: Ensuring functions work together seamlessly
• Clear documentation: Making the package accessible to non-developers
• Comprehensive testing: Validating functionality across different scenarios
• Continuous integration: Automating quality checks and deployment
• Version control: Managing changes and updates systematically

Documentation: Making Your Package Accessible

Using roxygen2 and pkgdown

Documentation is critical for package adoption and proper use:

#' Calculate risk difference with confidence intervals
#'
#' @param group1 Vector of outcomes for first group
#' @param group2 Vector of outcomes for second group
#' @param conf.level Confidence level (default: 0.95)
#' @return A list with risk difference and Cis
#' @export
risk_diff <- function(group1, group2, conf.level = 0.95) {
 # Function implementation
}

usethis::use_pkgdown()
pkgdown::build_site()

Ensuring Package Quality

Unit Testing and Validation

Quality assurance is essential for pharmaceutical applications:

Continuous Integration and Deployment

Automating Quality Checks

CI/CD reduces manual work while improving quality:

Dependency Management

Balancing Functionality and Stability

Managing dependencies is critical for long-term maintenance:

• Minimize external dependencies when possible
• Specify version requirements for critical dependencies
• Consider internal functions instead of importing from less stable packages
• Document dependency rationale for future maintainers